Tumor cell isolines

ABSTRACT

Methods and systems for processing a scanned tissue section include locating cells within a scanned tissue. Cells in the scanned tissue are classified using a classifier model. A tumor-cell ratio (TCR) map is generated based on classified normal cells and tumor cells. A TCR isoline is generated for a target TCR value using the TCR map, marking areas of the tissue section where a TCR is at or above the target TCR value. Dissection is performed on the tissue sample to isolate an area identified by the isoline.

RELATED APPLICATION INFORMATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/170,649, filed on Apr. 5, 2021, incorporated herein by referencein its entirety.

BACKGROUND Technical Field

The present invention relates to tumor cell detection andclassification, and, more particularly, to identifying regions within aslide image that relate to tumor cells of respective densities.

Description of the Related Art

Obtaining a tumor cell ratio, which is a measure of canceraggressiveness, includes counting a number of cells in the tumor area.For some tissue samples, the number of cells can be in the millions.Counting so many cells is not feasible for a human to perform, andestimation techniques tend to be inaccurate.

SUMMARY

A method for processing a scanned tissue section includes locating cellswithin a scanned tissue. Cells in the scanned tissue are classifiedusing a classifier model. A tumor-cell ratio (TCR) map is generatedbased on classified normal cells and tumor cells. A TCR isoline isgenerated for a target TCR value using the TCR map, marking areas of thetissue section where a TCR is at or above the target TCR value.Dissection is performed on the tissue sample to isolate an areaidentified by the isoline.

A system for processing a scanned tissue section includes a hardwareprocessor, a micro-dissection machine, and a memory that stores acomputer program. When executed by the hardware processor, the computerprogram causes the hardware processor to locate cells within a scannedtissue, to classify cells in the scanned tissue within the scannedtissue sample using a classifier model, to generate a tumor-cell ratio(TCR) map based on classified normal cells and tumor cells, to generatea TCR isoline for a TCR value using the TCR map, marking areas of thetissue section where a TCR is at or above the TCR value, and to performdissection on the tissue sample to isolate an area identified by theisoline using the micro-dissection machine.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block diagram of the processing of a tissue sample using awhole slide scanner and tumor cell ratio (TCR) isolines, in accordancewith an embodiment of the present invention;

FIG. 2 is a block/flow diagram of a method of analyzing scanned tissuesamples to generate TCR isolines, in accordance with an embodiment ofthe present invention;

FIG. 3 is a block/flow diagram of a method for training and using amodel to classify cells within a tissue sample and to identify isolineswithin the tissue sample for use in micro-dissection of the tissuesample, in accordance with an embodiment of the present invention;

FIG. 4 is an image of a tissue sample that includes isolines at variousdifferent TCR values, in accordance with an embodiment of the presentinvention;

FIG. 5 is a block/flow diagram of a method of generating TCR isolines,in accordance with an embodiment of the present invention;

FIG. 6 is a block diagram of a slide analysis system that may identifyTCR isolines and that may further use the identified TCR isolines toperform micro-dissection, in accordance with an embodiment of thepresent invention;

FIG. 7 is a diagram of an exemplary neural network architecture that maybe used as part of a neural network model, in accordance with anembodiment of the present invention; and

FIG. 8 is a diagram of an exemplary deep neural network architecturethat may be used as part of a neural network mode, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

To identify a tumor cell ratio (TCR), normal and tumor cells may beautomatically counted for a given area of a tissue sample.High-magnification images may be used to show details of individualcells, while lower-magnification images reveal how cells are arranged inlarger structures, such as glands. Cancerous cells can be contrastedfrom healthy cells by their individual cell features, by the way theyarrange themselves, or both.

Identifying TCR can help guide the selection of tissue areas for geneticpanel tests. Such tests may need a minimum percentage of tumor cells(e.g., at least around 25%) for genetic sequencing to be successful.Because genetic tests are expensive, time-consuming, and destructive, itis important to select a piece of tissue that has enough tumor cells torun the test. Using a marked area of a slide, the tissue can bemicro-dissected to isolate the desired area for testing. Thismicro-dissection can be performed automatically, for example usinglasers to isolate a marked area.

While tissue selection may be done manually, for example by visualestimation, human operators may inaccurately estimate TCR, particularlyfor regions of tissue where the overall TCR is low. However, machinelearning models may be used to detect and classify all cells within anarea, making it possible to quantitatively calculate the TCR at eachposition on a slide. Areas may be then determined having TCR above givenvalues. For example, areas may be identified that include at least 30%tumor cells. These areas may be defined by contour lines along which theTCR stays at a constant value (similar to contour showing lines ofconstant altitude in topographic maps). These contour lines may bereferred to herein as “isolines,” and may be used to identify regionswithin which the TCR is at or above a given value.

Using such TCR isolines, a pathologist or technician need not estimatean area of high TCR by eye, making selection of tissue areas moreobjective and accurate. The identified tissue areas may then be manuallycut or may be automatically sent to a micro-dissection machine. Althoughmicro-dissection machines operate from masks and can laser cut areas assmall as a single cell, the TCR isolines generated herein can be easilyinterpreted by both a human and a machine.

The inputs to the machine learning model may include a slide file, forexample generated by a whole-slide imaging (WSI) scanner. The slideimage may be split into a grid of tiles to best fit the processinghardware. The output may include reports of the locations of allcancerous cells and normal cells, as well as the TCR for each tile, andthe aggregated TCR for specified areas or the entire slide. Isolines maybe calculated to identify regions where the TCR is at or larger thangiven values. Visuals can include color scaling for each tile, forexample with red indicating a relatively high ratio of tumor cells andblue indicating a low ratio of tumor cells. Each individual tumor cellmay be highlighted to indicated where tumor cells are proliferating.Isolines may be drawn as contour line or region hatching overlays withcolor-coding (or with dash/hatch-style coding) representing the TCRwithin the area they enclose.

Referring now in detail to the figures in which like numerals representthe same or similar elements and initially to FIG. 1, a system foranalyzing tissue samples and providing TCR reports is shown. A tissuesample 102 is taken and put on a slide. The slide is scanned by awhole-slide scanner, producing a slide image, which is stored in a slidedatabase 106.

Slide analysis 108 may split each image into processing tiles, which maybe analyzed to identify cells and tumor cells according to a regulargrid and/or may be determined according to a user's indication ofsections of the slide image to focus on.

The slide analysis 108 may generate a report 110 that characterizes theinformation gleaned from the slide 102, for example including TCR,locations of cancerous cells, etc. This report 110 may be used bymedical professionals to help diagnose a patient, to identify a type andextent of a cancer, to identify a course of treatment, etc. The report110 may further include isoline information that identifies regions ofthe tissue where the TCR is at or above given values.

Once the report 110 has been generated, it can be used to identifyregions of the slide 102 for micro-dissection. For example, amicro-dissection machine 112 may automatically isolate a region havinghigh TCR values based on the isolines, and the isolated region maysubsequently be used for genetic testing.

Referring now to FIG. 2, a method of performing analysis 200 on ascanned slide is shown. Block 202 identifies a tissue sample within aslide image. This may include, for example, analyzing the whole slide ata relatively low resolution to identify the tissue sample via, e.g.,border detection. This may also include identifying marks made by a userthat indicate an area of the image to focus on, including marks madeusing a marker on the slide 102 itself, or marks made digitally on theslide image.

Block 204 generates processing tiles from the identified tissue sample.This may include, for example, generating a regular grid of squareregions, though it should be understood that any appropriate subdivisionof the image may be used. The user may additionally eliminate a portionof the tiles to prevent further analysis of those regions. The tiles maybe used to split the workload among multiple processors or processorcores, thereby increasing the speed of processing.

The processing tiles are supplied as inputs to the neural network modelin block 208. The model may be a fully-convolutional neural networktrained to detect cells and classify them as normal or cancerous. Thearchitecture of the model can include any number of layers in anyrelevant arrangement. The fully-convolutional model receives an inputimage and produces a set of maps as output that can be interpreted tolocate and classify cells.

The training of the model produces a trained model with thresholds. Thetrained model may output maps that can be interpreted, using the trainedthresholds, to locate and classify all cells in an input image as beingnormal or cancerous. In block 210, the output maps and detectionthreshold are used to identify the location of cells. In block 212, thelocation of the detected cells and the output maps are used to assignscores to each cell, with the score representing a probabilistic valueof the likelihood that a given cell is cancerous. For example, theprobabilistic value may range from 0 to 1, and may be is calculated inblock 214. A value close to 0 may indicate a high probability of being anormal cell, while at the other end of the range, a value close to 1 mayindicate a high probability of being a cancerous cell. A classificationthreshold obtained during model training may be used to classify eachcell as normal, with a probability score below the threshold, or cancer,with a probability score equal or above the threshold). Once the cellshave been classified in block 214, block 216 generates isolines. Anisoline may be formed by determining a contour along which the TCR is ata constant value. Each isoline may enclose one or more areas of theslide image.

Referring now to FIG. 3, an overall process is shown. Block 302generates training data for the machine learning model. This trainingdata may include a set of regions of interest from a set of patients'scanned tissue sample slides, representative of a particular conditionas encountered in clinical practice. The regions of interest may beannotated by domain experts, such as pathologists, to identify thelocation of all cells, including identification of regions that includea tumor. The locations of cells may be labeled by respective points atthe center of each cell nucleus, while tumor regions may be delineatedby freehand contours. Block 304 trains a fully convolutional neuralnetwork model, using the generated training patches. Any appropriatefully convolutional architecture may be used for the model.

Block 306 collects tissue samples from a patient. In some cases, thistissue sample may represent a biopsy sample of suspected canceroustissue. The tissue sample may be put onto a slide, which may be scannedusing whole-slide imaging in block 308.

Block 200 may then analyze the scanned samples, for example as describedabove in relation to FIG. 2. This analysis may generate isolines thatidentify regions of particular TCR values. Block 311 performsmicro-dissection of the tissue sample, for example to isolate a portionof the tissue sample having a relatively high TCR. This micro-dissectedsample may then be used in block 312 to create and administer atreatment to a patient.

For example, block 312 may determine information about the canceroustissue that can help identify the type of cancer and potentialtreatments. Administering such a treatment may include performingsurgery or administering pharmaceutical substances, such aschemotherapy.

Referring now to FIG. 4, an exemplary slide image 400 is shown after theprocessing tiles are generated in block 204. The slide image 400includes the scanned tissue sample 402. A number of tiles 404 have beenidentified within the image, in this case by applying a grid over thescanned tissue image 402, with pixels falling within each respectivegrid making up a respective tile. A set of markers 406 are also shown,having been provided by a human operator, to limit the tiles, such thattiles 404 are only determined within a boundary that is established bythe markers 406.

Each tile 404 may be separately processed, for example using parallelprocessing across multiple processors or processor cores. The totalnumber of tiles may be divided by the number of available processingthreads to form sets of tiles. Each set may be processed in serial by arespective thread.

Isolines 408 are shown in dashed lines. The differently dashed isolines408 indicate areas of differing TCR values. Thus, a first isoline (shownwith relatively long dashes) may represent regions of a relatively lowTCR value (e.g., about 20%), a second isoline (shown with medium dashes)may represent regions of a middle TCR value (e.g., about 25%), and athird isoline (shown with relatively short dashes) may represent regionsof a relatively high TCR value (e.g., about 30%).

Referring now to FIG. 5, additional detail is shown on the generation ofisolines in block 216. This process is analogous to creating ageographical topographic map, where contour lines (or isolines)represent a path along which the altitude is constant. A slide TCR mapcan be used to calculate isolines representing a path along which theTCR is at a constant value and that bound areas within which the TCR isat or above that value. Hence the first step is to generate a TCR map.

In an exemplary approach, block 502 initializes (e.g., sets all pixelsof the map to 0) two overview maps. Each overview map represents anentire analyzed area of the slide at a lower resolution than theoriginal image. The resolution may be selected by lancing speed againstaccuracy. Lower resolutions will generate coarse isolines quickly, whilehigher resolutions will take longer to compute, but will produce smoothand precise isolines. The highest useful resolution is where the size ofa cell is about 1 pixel—resolutions above that will not provide anyimprovement in accuracy. The first overview map is for all cells and thesecond is for tumor cells only.

Block 504 draws cells onto the maps. Normal cells may be drawn into thefirst map only, while tumor cells are drawn into both maps. The processof drawing a cell at the highest resolution (e.g., one cell per pixel)may include setting a value of the location of that cell to 1. For lowerresolution maps, a fraction is calculated corresponding to the size ofthe cell and is accumulated at the location of the cell. Block 506computes respective maps by performing smoothing. Smoothing may beperformed by local averaging or by convolving Gaussian kernels with themaps.

Smoothing helps to ensure that the resulting TCR map is not overlynoisy, which can prevent extraction of a meaningful contour isoline.Although laser micro-dissection machines can micro-dissect tissue areasdirectly from mask files in any shape, simple smooth isolines may begenerated to encompass regions of high TCR without holes. In cases wherea micro-dissection machine is not available, a human technician mayperform the dissection and may benefit from a simple, easilyinterpretable guide.

Smoothing is akin to local averaging, and its intensity can becontrolled by the size of the averaging kernel. A small size kernel,such as a 3×3 kernel, will exert a very localized smoothing, resultingin a map that is still very noisy. On the other hand, larger kernelsizes, such as 100×100, would result in a very smooth map, but one thatmay have lost some of its useful features. The kernel size shouldcontain enough cells to obtain a meaningful averaging, for examplebetween 100 and 400 cells. Thus, with a resolution of 1 pixel per cell,a kernel size of 10×10 to about 20×20 may be used.

Block 508 then determines a ratio between the tumor-cell map and theall-cell map, normalizing the values to a range between zero and one toproduce a TCR map. This may be performed by taking a component-wiseratio of the tumor-cells map over the all-cells map.

In block 510, one or more isoline(s) can be iteratively found for one ormore given target TCR value(s) by using the smoothed and normalized TCRmap as a guide to generate candidate isolines, searching for isolineswhere the identified TCR is close to a target TCR. Because the TCR maphas been smoothed and normalized, its values may no longer be actual TCRvalues. Also, a smoothed TCR map may include many peaks and valleys andisoline contours may fully encompass valleys, creating holes within thecontour. Hence, TCR isolines that correspond to the desired targetactual TCR value may be identified.

For example, slide areas that have at least 25% tumor cells may be ofinterest, with a tolerance of about 1%. A first candidate isoline on thesmoothed TCR map may be extracted at this value (25%). Isolineextraction from the smoothed TCR map include thresholding the map at thegiven value and extracting the contour of connected pixels. Thisoperation may generate several distinct regions of connected pixels, andan isoline may therefore be represented by several contours. Using theisoline contour(s), all cells are selected that fall within thecontour(s). The actual TCR of those selected cells may then becalculated.

For example, consider a case where the actual TCR is 22%. This issmaller than the desired TCR of 25%, and outside of the tolerance range.The working value may be increased by an increment dT, such as 5%. Theworking TCR may then be 30% and thresholding of the TCR map may berepeated to extract an isoline. Calculating the actual TCR, an exemplaryvalue of 27% may be found, which is now larger than the target. Theincrement may be adjusted, for example by half, and the process may berepeated. Thus, following the example with dT=−2.5%, so the working TCRbecomes 30%−2.5%=27.5%. The actual TCR may be calculated again, and inthis example may come to 25.5%, which is within the 1% tolerance of thetarget TCR. This stops the iterative process of block 510, and theisoline may be output in block 512. Block 510 may be repeated foradditional isoline values. In some situations, it is possible that thissearch could continue indefinitely, when the actual TCR stays stable butis not close enough to the target. In this case, a different stoppingcriterion may be to stop when a minimum value of dT is reached. Isolinesmay be outputted in the form of a vector of two-dimensional slidecoordinates defining its contour, for example into a file having aformat that can be used by a micro-dissection apparatus. The isolinesmay also be drawn onto an image overview of the slide image to visuallyguide a pathologist or a technician for a manual micro-dissection of thetissue.

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

Each computer program may be tangibly stored in a machine-readablestorage media or device (e.g., program memory or magnetic disk) readableby a general or special purpose programmable computer, for configuringand controlling operation of a computer when the storage media or deviceis read by the computer to perform the procedures described herein. Theinventive system may also be considered to be embodied in acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform the functions describedherein.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

As employed herein, the term “hardware processor subsystem” or “hardwareprocessor” can refer to a processor, memory, software or combinationsthereof that cooperate to perform one or more specific tasks. In usefulembodiments, the hardware processor subsystem can include one or moredata processing elements (e.g., logic circuits, processing circuits,instruction execution devices, etc.). The one or more data processingelements can be included in a central processing unit, a graphicsprocessing unit, and/or a separate processor- or computing element-basedcontroller (e.g., logic gates, etc.). The hardware processor subsystemcan include one or more on-board memories (e.g., caches, dedicatedmemory arrays, read only memory, etc.). In some embodiments, thehardware processor subsystem can include one or more memories that canbe on or off board or that can be dedicated for use by the hardwareprocessor subsystem (e.g., ROM, RAM, basic input/output system (BIOS),etc.).

In some embodiments, the hardware processor subsystem can include andexecute one or more software elements. The one or more software elementscan include an operating system and/or one or more applications and/orspecific code to achieve a specified result.

In other embodiments, the hardware processor subsystem can includededicated, specialized circuitry that performs one or more electronicprocessing functions to achieve a specified result. Such circuitry caninclude one or more application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), and/or programmable logic arrays(PLAs).

These and other variations of a hardware processor subsystem are alsocontemplated in accordance with embodiments of the present invention.

Referring now to FIG. 6, a slide analysis system 600 is shown. Thesystem 600 includes a hardware processor 602 and memory 604. The system600 may include a number of functional modules, which may be implementedas software that is stored in the memory 604 and that is executed by thehardware processor 602. In some cases, some of the functional modulesmay be implemented as discrete hardware components, for example in theform of ASICs or FPGAs.

A slide database 606 may be stored in the memory 604, and may be used bya model trainer 608 to train a fully-convolutional model. The slidedatabase 606 may be made up of images that have been scanned andannotated.

A network interface 605 receives a new slide image by any appropriatewired or wireless communications medium or protocol. For example, thenew slide image may be received from a whole-slide scanner 104. Anyappropriate type of interface may be implemented, including a generalpurpose computer network interface or a dedicated interface for thewhole-slide scanner 104.

The new slide image is processed by slide analysis 610. A reportgenerator 612 uses the output of the slide analysis 610, for exampleidentifying isolines using the output maps and rendering the isolines asan overlay on the image. A micro-dissector 614 can then performmicro-dissection on the sample, using the identified isolines to isolatea portion of the tissue sample having an appropriate TCR.

An artificial neural network (ANN) is an information processing systemthat is inspired by biological nervous systems, such as the brain. Thekey element of ANNs is the structure of the information processingsystem, which includes a large number of highly interconnectedprocessing elements (called “neurons”) working in parallel to solvespecific problems. ANNs are furthermore trained using a set of trainingdata, with learning that involves adjustments to weights that existbetween the neurons. An ANN is configured for a specific application,such as pattern recognition or data classification, through such alearning process.

Referring now to FIGS. 7 and 8, exemplary neural network architecturesare shown, which may be used to implement parts of the present models. Aneural network is a generalized system that improves its functioning andaccuracy through exposure to additional empirical data. The neuralnetwork becomes trained by exposure to the empirical data. Duringtraining, the neural network stores and adjusts a plurality of weightsthat are applied to the incoming empirical data. By applying theadjusted weights to the data, the data can be identified as belonging toa particular predefined class from a set of classes or a probabilitythat the inputted data belongs to each of the classes can be outputted.

The empirical data, also known as training data, from a set of examplescan be formatted as a string of values and fed into the input of theneural network. Each example may be associated with a known result oroutput. Each example can be represented as a pair, (x, y), where xrepresents the input data and y represents the known output. The inputdata may include a variety of different data types, and may includemultiple distinct values. The network can have one input node for eachvalue making up the example's input data, and a separate weight can beapplied to each input value. The input data can, for example, beformatted as a vector, an array, or a string depending on thearchitecture of the neural network being constructed and trained.

The neural network “learns” by comparing the neural network outputgenerated from the input data to the known values of the examples, andadjusting the stored weights to minimize the differences between theoutput values and the known values. The adjustments may be made to thestored weights through back propagation, where the effect of the weightson the output values may be determined by calculating the mathematicalgradient and adjusting the weights in a manner that shifts the outputtowards a minimum difference. This optimization, referred to as agradient descent approach, is a non-limiting example of how training maybe performed. A subset of examples with known values that were not usedfor training can be used to test and validate the accuracy of the neuralnetwork.

During operation, the trained neural network can be used on new datathat was not previously used in training or validation throughgeneralization. The adjusted weights of the neural network can beapplied to the new data, where the weights estimate a function developedfrom the training examples. The parameters of the estimated functionwhich are captured by the weights are based on statistical inference.

In layered neural networks, nodes are arranged in the form of layers. Anexemplary simple neural network has an input layer 720 of source nodes722, and a single computation layer 730 having one or more computationnodes 732 that also act as output nodes, where there is a singlecomputation node 732 for each possible category into which the inputexample could be classified. An input layer 720 can have a number ofsource nodes 722 equal to the number of data values 712 in the inputdata 710. The data values 712 in the input data 710 can be representedas a column vector. Each computation node 732 in the computation layer730 generates a linear combination of weighted values from the inputdata 710 fed into input nodes 720, and applies a non-linear activationfunction that is differentiable to the sum. The exemplary simple neuralnetwork can perform classification on linearly separable examples (e.g.,patterns).

A deep neural network, such as a multilayer perceptron, can have aninput layer 720 of source nodes 722, one or more computation layer(s)730 having one or more computation nodes 732, and an output layer 740,where there is a single output node 742 for each possible category intowhich the input example could be classified. An input layer 720 can havea number of source nodes 722 equal to the number of data values 712 inthe input data 710. The computation nodes 732 in the computationlayer(s) 730 can also be referred to as hidden layers, because they arebetween the source nodes 722 and output node(s) 742 and are not directlyobserved. Each node 732, 742 in a computation layer generates a linearcombination of weighted values from the values output from the nodes ina previous layer, and applies a non-linear activation function that isdifferentiable over the range of the linear combination. The weightsapplied to the value from each previous node can be denoted, forexample, by w₁, w₂, . . . w_(n−1), w_(n). The output layer provides theoverall response of the network to the inputted data. A deep neuralnetwork can be fully connected, where each node in a computational layeris connected to all other nodes in the previous layer, or may have otherconfigurations of connections between layers. If links between nodes aremissing, the network is referred to as partially connected.

Training a deep neural network can involve two phases, a forward phasewhere the weights of each node are fixed and the input propagatesthrough the network, and a backwards phase where an error value ispropagated backwards through the network and weight values are updated.

The computation nodes 732 in the one or more computation (hidden)layer(s) 730 perform a nonlinear transformation on the input data 712that generates a feature space. The classes or categories may be moreeasily separated in the feature space than in the original data space.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment. However, it is to beappreciated that features of one or more embodiments can be combinedgiven the teachings of the present invention provided herein.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended for as many items listed.

The foregoing is to be understood as being in every respect illustrativeand exemplary, but not restrictive, and the scope of the inventiondisclosed herein is not to be determined from the Detailed Description,but rather from the claims as interpreted according to the full breadthpermitted by the patent laws. It is to be understood that theembodiments shown and described herein are only illustrative of thepresent invention and that those skilled in the art may implementvarious modifications without departing from the scope and spirit of theinvention. Those skilled in the art could implement various otherfeature combinations without departing from the scope and spirit of theinvention. Having thus described aspects of the invention, with thedetails and particularity required by the patent laws, what is claimedand desired protected by Letters Patent is set forth in the appendedclaims.

What is claimed is:
 1. A computer-implemented method for processing ascanned tissue section, comprising: locating cells within a scannedtissue; classifying cells in the scanned tissue using a classifiermodel; generating a tumor-cell ratio (TCR) map based on classifiednormal cells and tumor cells; generating a TCR isoline for a target TCRvalue using the TCR map, marking areas of the tissue section where a TCRis at or above the target TCR value; and performing dissection on thetissue sample to isolate an area identified by the isoline.
 2. Themethod of claim 1, wherein the classifier model is a fully-convolutionalneural-network model.
 3. The method of claim 1, wherein generating theTCR map comprises: identifying all cell locations in a first map of thetissue section; identifying tumor cell locations in a second map of thetissue section; smoothing the first map and the second map; generating aTCR map using a component-wise ratio of the second map over the firstmap.
 4. The method of claim 3, wherein the first map and the second maphave a resolution of one cell per pixel.
 5. The method of claim 3,wherein the first map and the second map have a resolution that is lessthan one cell per pixel and identifying cell locations and tumor celllocations includes accumulating a fractional value into pixelscorresponding to coordinate locations of respective cells.
 6. The methodof claim 3, wherein smoothing the first map and the second map includeslocal averaging.
 7. The method of claim 3, wherein smoothing of thefirst map and the second map includes convolving an averaging kernel. 8.The method of claim 7, wherein the averaging kernel is a Gaussiankernel.
 9. The method of claim 3, further comprising normalizing the TCRmap such that a smallest pixel value is 0 and a largest pixel valueis
 1. 10. The method of claim 1, wherein the generating the isoline forthe target TCR value includes an iterative process that comprises:setting an isoline on the TCR map using a threshold value; determiningan actual TCR of cells selected by the isoline; and adjusting thethreshold TCR value in accordance with a comparison between the actualTCR and the target TCR value.
 11. A system for processing a scannedtissue section, comprising: a hardware processor; a micro-dissectionmachine; and a memory that stores a computer program, which, whenexecuted by the hardware processor, causes the hardware processor to:locate cells within a scanned tissue; classify cells in the scannedtissue within the scanned tissue sample using a classifier model;generate a tumor-cell ratio (TCR) map based on classified normal cellsand tumor cells; generate a TCR isoline for a target TCR value using theTCR map, marking areas of the tissue section where a TCR is at or abovethe target TCR value; and perform dissection on the tissue sample toisolate an area identified by the isoline using the micro-dissectionmachine.
 12. The system of claim 11, wherein the classifier model is afully-convolutional neural-network model.
 13. The system of claim 11,wherein the computer program further causes the hardware processor to:identify all cell locations in a first map of the tissue section;identify tumor cell locations in a second map of the tissue section;smooth the first map and the second map; generate a TCR map using acomponent-wise ratio of the second map over the first map.
 14. Thesystem of claim 13, wherein the first map and the second map have aresolution of one cell per pixel.
 15. The system of claim 13, whereinthe first map and the second map have a resolution that is less than onecell per pixel and wherein the computer program further causes thehardware processor to accumulate a fractional value into pixelscorresponding to coordinate locations of respective cells to identifycells.
 16. The system of claim 13, wherein the computer program furthercauses the hardware processor to smooth the first map and the second mapusing local averaging.
 17. The system of claim 13, wherein the computerprogram further causes the hardware processor to smooth the first mapand the second map using convolution of an averaging kernel.
 18. Thesystem of claim 17, wherein the averaging kernel is a Gaussian kernel.19. The system of claim 13, wherein the computer program further causesthe hardware processor to normalize the TCR map such that a smallestpixel value is 0 and a largest pixel value is
 1. 20. The system of claim11, wherein the computer program further causes the hardware processorto iteratively: set an isoline on the TCR map using a threshold value;determine an actual TCR of cells selected by the isoline; and adjust thethreshold TCR value in accordance with a comparison between the actualTCR and a target TCR value.